dmlalg: Double Machine Learning Algorithms

Implementation of double machine learning (DML) algorithms in R, based on Emmenegger and Buehlmann (2021) "Regularizing Double Machine Learning in Partially Linear Endogenous Models" <arXiv:2101.12525> and Emmenegger and Buehlmann (2021) <arXiv:2108.13657> "Double Machine Learning for Partially Linear Mixed-Effects Models with Repeated Measurements". First part: our goal is to perform inference for the linear parameter in partially linear models with confounding variables. The standard DML estimator of the linear parameter has a two-stage least squares interpretation, which can lead to a large variance and overwide confidence intervals. We apply regularization to reduce the variance of the estimator, which produces narrower confidence intervals that are approximately valid. Nuisance terms can be flexibly estimated with machine learning algorithms. Second part: our goal is to estimate and perform inference for the linear coefficient in a partially linear mixed-effects model with DML. Machine learning algorithms allows us to incorporate more complex interaction structures and high-dimensional variables.

Version: 1.0.1
Depends: R (≥ 4.0.0), stats
Imports: glmnet, lme4, matrixcalc, methods, splines, randomForest
Suggests: testthat (≥ 3.0.0)
Published: 2021-09-02
Author: Corinne Emmenegger ORCID iD [aut, cre], Peter Buehlmann ORCID iD [ths]
Maintainer: Corinne Emmenegger <emmenegger at>
License: GPL (≥ 3)
NeedsCompilation: no
Citation: dmlalg citation info
Materials: README NEWS
CRAN checks: dmlalg results


Reference manual: dmlalg.pdf
Package source: dmlalg_1.0.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): dmlalg_1.0.1.tgz, r-release (x86_64): dmlalg_1.0.1.tgz, r-oldrel: dmlalg_1.0.1.tgz
Old sources: dmlalg archive


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